In [1]:
import pandas as pd #pandas (pd): Data manipulation ke liye use hota hai, jaise CSV file load karna aur process karna.

import plotly.express as px #plotly.express (px): Data visualization library jo easy aur quick plots banata hai.
import plotly.graph_objects as go #plotly.graph_objects (go): Advanced and customizable graphs banane ke liye
import plotly.io as pio   #plotly.io (pio): Graph templates ko customize karne ke liye
import plotly.colors as colors 
pio.templates.default = "plotly_white"  #pio.templates.default = "plotly_white": Default theme white rakha gaya hai graphs ke liye
In [2]:
data = pd.read_csv("Sample - Superstore.csv", encoding='latin-1') #encoding='latin-1': Special characters ko properly read karne ke liye encoding use hui hai
In [3]:
data
Out[3]:
Row ID Order ID Order Date Ship Date Ship Mode Customer ID Customer Name Segment Country City ... Postal Code Region Product ID Category Sub-Category Product Name Sales Quantity Discount Profit
0 1 CA-2016-152156 11/8/2016 11/11/2016 Second Class CG-12520 Claire Gute Consumer United States Henderson ... 42420 South FUR-BO-10001798 Furniture Bookcases Bush Somerset Collection Bookcase 261.9600 2 0.00 41.9136
1 2 CA-2016-152156 11/8/2016 11/11/2016 Second Class CG-12520 Claire Gute Consumer United States Henderson ... 42420 South FUR-CH-10000454 Furniture Chairs Hon Deluxe Fabric Upholstered Stacking Chairs,... 731.9400 3 0.00 219.5820
2 3 CA-2016-138688 6/12/2016 6/16/2016 Second Class DV-13045 Darrin Van Huff Corporate United States Los Angeles ... 90036 West OFF-LA-10000240 Office Supplies Labels Self-Adhesive Address Labels for Typewriters b... 14.6200 2 0.00 6.8714
3 4 US-2015-108966 10/11/2015 10/18/2015 Standard Class SO-20335 Sean O'Donnell Consumer United States Fort Lauderdale ... 33311 South FUR-TA-10000577 Furniture Tables Bretford CR4500 Series Slim Rectangular Table 957.5775 5 0.45 -383.0310
4 5 US-2015-108966 10/11/2015 10/18/2015 Standard Class SO-20335 Sean O'Donnell Consumer United States Fort Lauderdale ... 33311 South OFF-ST-10000760 Office Supplies Storage Eldon Fold 'N Roll Cart System 22.3680 2 0.20 2.5164
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
9989 9990 CA-2014-110422 1/21/2014 1/23/2014 Second Class TB-21400 Tom Boeckenhauer Consumer United States Miami ... 33180 South FUR-FU-10001889 Furniture Furnishings Ultra Door Pull Handle 25.2480 3 0.20 4.1028
9990 9991 CA-2017-121258 2/26/2017 3/3/2017 Standard Class DB-13060 Dave Brooks Consumer United States Costa Mesa ... 92627 West FUR-FU-10000747 Furniture Furnishings Tenex B1-RE Series Chair Mats for Low Pile Car... 91.9600 2 0.00 15.6332
9991 9992 CA-2017-121258 2/26/2017 3/3/2017 Standard Class DB-13060 Dave Brooks Consumer United States Costa Mesa ... 92627 West TEC-PH-10003645 Technology Phones Aastra 57i VoIP phone 258.5760 2 0.20 19.3932
9992 9993 CA-2017-121258 2/26/2017 3/3/2017 Standard Class DB-13060 Dave Brooks Consumer United States Costa Mesa ... 92627 West OFF-PA-10004041 Office Supplies Paper It's Hot Message Books with Stickers, 2 3/4" x 5" 29.6000 4 0.00 13.3200
9993 9994 CA-2017-119914 5/4/2017 5/9/2017 Second Class CC-12220 Chris Cortes Consumer United States Westminster ... 92683 West OFF-AP-10002684 Office Supplies Appliances Acco 7-Outlet Masterpiece Power Center, Wihtou... 243.1600 2 0.00 72.9480

9994 rows × 21 columns

In [4]:
data.describe()
Out[4]:
Row ID Postal Code Sales Quantity Discount Profit
count 9994.000000 9994.000000 9994.000000 9994.000000 9994.000000 9994.000000
mean 4997.500000 55190.379428 229.858001 3.789574 0.156203 28.656896
std 2885.163629 32063.693350 623.245101 2.225110 0.206452 234.260108
min 1.000000 1040.000000 0.444000 1.000000 0.000000 -6599.978000
25% 2499.250000 23223.000000 17.280000 2.000000 0.000000 1.728750
50% 4997.500000 56430.500000 54.490000 3.000000 0.200000 8.666500
75% 7495.750000 90008.000000 209.940000 5.000000 0.200000 29.364000
max 9994.000000 99301.000000 22638.480000 14.000000 0.800000 8399.976000
In [5]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9994 entries, 0 to 9993
Data columns (total 21 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Row ID         9994 non-null   int64  
 1   Order ID       9994 non-null   object 
 2   Order Date     9994 non-null   object 
 3   Ship Date      9994 non-null   object 
 4   Ship Mode      9994 non-null   object 
 5   Customer ID    9994 non-null   object 
 6   Customer Name  9994 non-null   object 
 7   Segment        9994 non-null   object 
 8   Country        9994 non-null   object 
 9   City           9994 non-null   object 
 10  State          9994 non-null   object 
 11  Postal Code    9994 non-null   int64  
 12  Region         9994 non-null   object 
 13  Product ID     9994 non-null   object 
 14  Category       9994 non-null   object 
 15  Sub-Category   9994 non-null   object 
 16  Product Name   9994 non-null   object 
 17  Sales          9994 non-null   float64
 18  Quantity       9994 non-null   int64  
 19  Discount       9994 non-null   float64
 20  Profit         9994 non-null   float64
dtypes: float64(3), int64(3), object(15)
memory usage: 1.6+ MB

Converting Date Columns¶

In [6]:
data['Order Date']=pd.to_datetime(data['Order Date'])
data['Ship Date'] = pd.to_datetime(data['Ship Date']) 
#Date Conversion: Order Date aur Ship Date columns ko datetime format me convert kiya gaya hai for date-based analysis.
In [7]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9994 entries, 0 to 9993
Data columns (total 21 columns):
 #   Column         Non-Null Count  Dtype         
---  ------         --------------  -----         
 0   Row ID         9994 non-null   int64         
 1   Order ID       9994 non-null   object        
 2   Order Date     9994 non-null   datetime64[ns]
 3   Ship Date      9994 non-null   datetime64[ns]
 4   Ship Mode      9994 non-null   object        
 5   Customer ID    9994 non-null   object        
 6   Customer Name  9994 non-null   object        
 7   Segment        9994 non-null   object        
 8   Country        9994 non-null   object        
 9   City           9994 non-null   object        
 10  State          9994 non-null   object        
 11  Postal Code    9994 non-null   int64         
 12  Region         9994 non-null   object        
 13  Product ID     9994 non-null   object        
 14  Category       9994 non-null   object        
 15  Sub-Category   9994 non-null   object        
 16  Product Name   9994 non-null   object        
 17  Sales          9994 non-null   float64       
 18  Quantity       9994 non-null   int64         
 19  Discount       9994 non-null   float64       
 20  Profit         9994 non-null   float64       
dtypes: datetime64[ns](2), float64(3), int64(3), object(13)
memory usage: 1.6+ MB

Adding New Date-Based Columns¶

In [8]:
data['Order Month'] = data['Order Date'].dt.month 
data['Order Year'] = data['Order Date'].dt.year
data['Order Day of Week'] = data['Order Date'].dt.dayofweek
#Order Month: Order date se month extract karte hain.
#Order Year: Order date se year extract hota hai.
#Order Day of Week: Week ka day (0 for Monday, 6 for Sunday) extract kiya gaya hai
In [9]:
data.head()
Out[9]:
Row ID Order ID Order Date Ship Date Ship Mode Customer ID Customer Name Segment Country City ... Category Sub-Category Product Name Sales Quantity Discount Profit Order Month Order Year Order Day of Week
0 1 CA-2016-152156 2016-11-08 2016-11-11 Second Class CG-12520 Claire Gute Consumer United States Henderson ... Furniture Bookcases Bush Somerset Collection Bookcase 261.9600 2 0.00 41.9136 11 2016 1
1 2 CA-2016-152156 2016-11-08 2016-11-11 Second Class CG-12520 Claire Gute Consumer United States Henderson ... Furniture Chairs Hon Deluxe Fabric Upholstered Stacking Chairs,... 731.9400 3 0.00 219.5820 11 2016 1
2 3 CA-2016-138688 2016-06-12 2016-06-16 Second Class DV-13045 Darrin Van Huff Corporate United States Los Angeles ... Office Supplies Labels Self-Adhesive Address Labels for Typewriters b... 14.6200 2 0.00 6.8714 6 2016 6
3 4 US-2015-108966 2015-10-11 2015-10-18 Standard Class SO-20335 Sean O'Donnell Consumer United States Fort Lauderdale ... Furniture Tables Bretford CR4500 Series Slim Rectangular Table 957.5775 5 0.45 -383.0310 10 2015 6
4 5 US-2015-108966 2015-10-11 2015-10-18 Standard Class SO-20335 Sean O'Donnell Consumer United States Fort Lauderdale ... Office Supplies Storage Eldon Fold 'N Roll Cart System 22.3680 2 0.20 2.5164 10 2015 6

5 rows × 24 columns

Monthly Sales Analysis¶

In [10]:
sales_by_month = data.groupby('Order Month')['Sales'].sum().reset_index()
In [11]:
sales_by_month
Out[11]:
Order Month Sales
0 1 94924.8356
1 2 59751.2514
2 3 205005.4888
3 4 137762.1286
4 5 155028.8117
5 6 152718.6793
6 7 147238.0970
7 8 159044.0630
8 9 307649.9457
9 10 200322.9847
10 11 352461.0710
11 12 325293.5035
In [12]:
fig = px.line(sales_by_month, 
              x='Order Month', 
              y='Sales', 
              title='Monthly Sales Analysis')
fig.show()
In [13]:
#Data Grouping:
#data.groupby('Order Month')['Sales'].sum() se har month ki total sales nikalte hain.
#.reset_index() data ko structured format me rakhta hai.
#px.line: Monthly sales trend show karne ke liye line chart banaya gaya hai.
#fig.show(): Graph display karta hai.

Sales Analysis by Category¶

In [14]:
sales_by_category = data.groupby('Category')['Sales'].sum().reset_index()
In [15]:
sales_by_category
Out[15]:
Category Sales
0 Furniture 741999.7953
1 Office Supplies 719047.0320
2 Technology 836154.0330
In [17]:
fig = px.pie(sales_by_category, 
             values='Sales', 
             names='Category', 
             hole=0.5, 
             color_discrete_sequence=px.colors.qualitative.Pastel)

fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(title_text='Sales Analysis by Category', title_font=dict(size=24))

fig.show()
In [18]:
#groupby('Category'): Category-wise sales nikalte hain.
#Pie Chart:
#px.pie: Sales proportions ko pie chart me show karta hai.
#hole=0.5: Donut-style chart banata hai.
#Pastel Colors: Chart me soft color palette use kiya gaya hai.

Sales Analysis by Sub-Category¶

In [19]:
sales_by_subcategory = data.groupby('Sub-Category')['Sales'].sum().reset_index()
In [21]:
sales_by_subcategory
Out[21]:
Sub-Category Sales
0 Accessories 167380.3180
1 Appliances 107532.1610
2 Art 27118.7920
3 Binders 203412.7330
4 Bookcases 114879.9963
5 Chairs 328449.1030
6 Copiers 149528.0300
7 Envelopes 16476.4020
8 Fasteners 3024.2800
9 Furnishings 91705.1640
10 Labels 12486.3120
11 Machines 189238.6310
12 Paper 78479.2060
13 Phones 330007.0540
14 Storage 223843.6080
15 Supplies 46673.5380
16 Tables 206965.5320
In [20]:
fig = px.bar(sales_by_subcategory, 
             x='Sub-Category', 
             y='Sales', 
             title='Sales Analysis by Sub-Category')
fig.show()

Monthly Profit Analysis¶

In [22]:
profit_by_month = data.groupby('Order Month')['Profit'].sum().reset_index()
In [23]:
profit_by_month
Out[23]:
Order Month Profit
0 1 9134.4461
1 2 10294.6107
2 3 28594.6872
3 4 11587.4363
4 5 22411.3078
5 6 21285.7954
6 7 13832.6648
7 8 21776.9384
8 9 36857.4753
9 10 31784.0413
10 11 35468.4265
11 12 43369.1919
In [24]:
fig = px.line(profit_by_month, 
              x='Order Month', 
              y='Profit', 
              title='Monthly Profit Analysis')
fig.show()
In [25]:
profit_by_category = data.groupby('Category')['Profit'].sum().reset_index()
In [26]:
profit_by_category
Out[26]:
Category Profit
0 Furniture 18451.2728
1 Office Supplies 122490.8008
2 Technology 145454.9481
In [27]:
fig = px.pie(profit_by_category, 
             values='Profit', 
             names='Category', 
             hole=0.5, 
             color_discrete_sequence=px.colors.qualitative.Pastel)

fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(title_text='Profit Analysis by Category', title_font=dict(size=24))

fig.show()

Profit Analysis by Sub-Category¶

In [29]:
profit_by_subcategory = data.groupby('Sub-Category')['Profit'].sum().reset_index()
In [30]:
profit_by_subcategory
Out[30]:
Sub-Category Profit
0 Accessories 41936.6357
1 Appliances 18138.0054
2 Art 6527.7870
3 Binders 30221.7633
4 Bookcases -3472.5560
5 Chairs 26590.1663
6 Copiers 55617.8249
7 Envelopes 6964.1767
8 Fasteners 949.5182
9 Furnishings 13059.1436
10 Labels 5546.2540
11 Machines 3384.7569
12 Paper 34053.5693
13 Phones 44515.7306
14 Storage 21278.8264
15 Supplies -1189.0995
16 Tables -17725.4811
In [31]:
fig = px.bar(profit_by_subcategory, x='Sub-Category', 
             y='Profit', 
             title='Profit Analysis by Sub-Category')
fig.show()
In [32]:
data.head()
Out[32]:
Row ID Order ID Order Date Ship Date Ship Mode Customer ID Customer Name Segment Country City ... Category Sub-Category Product Name Sales Quantity Discount Profit Order Month Order Year Order Day of Week
0 1 CA-2016-152156 2016-11-08 2016-11-11 Second Class CG-12520 Claire Gute Consumer United States Henderson ... Furniture Bookcases Bush Somerset Collection Bookcase 261.9600 2 0.00 41.9136 11 2016 1
1 2 CA-2016-152156 2016-11-08 2016-11-11 Second Class CG-12520 Claire Gute Consumer United States Henderson ... Furniture Chairs Hon Deluxe Fabric Upholstered Stacking Chairs,... 731.9400 3 0.00 219.5820 11 2016 1
2 3 CA-2016-138688 2016-06-12 2016-06-16 Second Class DV-13045 Darrin Van Huff Corporate United States Los Angeles ... Office Supplies Labels Self-Adhesive Address Labels for Typewriters b... 14.6200 2 0.00 6.8714 6 2016 6
3 4 US-2015-108966 2015-10-11 2015-10-18 Standard Class SO-20335 Sean O'Donnell Consumer United States Fort Lauderdale ... Furniture Tables Bretford CR4500 Series Slim Rectangular Table 957.5775 5 0.45 -383.0310 10 2015 6
4 5 US-2015-108966 2015-10-11 2015-10-18 Standard Class SO-20335 Sean O'Donnell Consumer United States Fort Lauderdale ... Office Supplies Storage Eldon Fold 'N Roll Cart System 22.3680 2 0.20 2.5164 10 2015 6

5 rows × 24 columns

Sales and Profit Analysis by Customer Segment¶

In [33]:
sales_profit_by_segment = data.groupby('Segment').agg({'Sales': 'sum', 'Profit': 'sum'}).reset_index()
color_palette = colors.qualitative.Pastel

fig = go.Figure()
fig.add_trace(go.Bar(x=sales_profit_by_segment['Segment'], 
                     y=sales_profit_by_segment['Sales'], 
                     name='Sales',
                     marker_color=color_palette[0]))

fig.add_trace(go.Bar(x=sales_profit_by_segment['Segment'], 
                     y=sales_profit_by_segment['Profit'], 
                     name='Profit',
                     marker_color=color_palette[1]))

fig.update_layout(title='Sales and Profit Analysis by Customer Segment',
                  xaxis_title='Customer Segment', yaxis_title='Amount')

fig.show()

analyse sales-to-profit ratio¶

In [34]:
sales_profit_by_segment = data.groupby('Segment').agg({'Sales': 'sum', 'Profit': 'sum'}).reset_index()
sales_profit_by_segment['Sales_to_Profit_Ratio'] = sales_profit_by_segment['Sales'] / sales_profit_by_segment['Profit']
print(sales_profit_by_segment[['Segment', 'Sales_to_Profit_Ratio']])

       Segment  Sales_to_Profit_Ratio
0     Consumer               8.659471
1    Corporate               7.677245
2  Home Office               7.125416
In [ ]: